The Data Scientist Speaks: The Challenges of Chatbots For Live Chat Sales

Before chatbots for live chat sales, there were bots: a piece of software that was designed and developed to automate a particular task. A chatbot is built on the same premise as a bot, revolving around chat or simulated conversation. A chatbot uses machine learning to pick up on conversational cadences, allowing the bot to mimic human conversation and react to spoken and written prompts to deliver a service. These personal assistants, also known as chatbots, work through conversational interfaces like voice and instant messaging, and are effective tools for live chat sales. The idea is to build more natural interfaces for people to access information services and perform complicated online tasks. Its language-based user interface can be plugged into a number of data sources via APIs to deliver information or services on demand, such as weather forecasts, breaking news, etc. According to Lauren Kunze, Principal at Pandorabots, there are two classes of chatbots: utility chatbots and content driven chatbots. Utility chatbots carry out a specific task following a prompt. At higher levels, the more entertainment- related chatbots are able to answer all questions and get things done like Siri and Cortana.

Chatbots could soon be reading news articles and then discussing those with us. Voice-activated assistants such as Apple’s Siri or Amazon’s Alexa can check the weather for us, but remain stumped by more complicated conversations. There are many data science researchers today who are working to create a chatbot that can understand a news or Wikipedia article and then talk about it with a human. The modus operandi of bot engagement is that people don’t necessarily need to feel as if they are interacting with a human, but they do need to feel as if they are being heard. Data Scientists are facing a number of challenges as they strive to develop a successful chatbot with maximum efficiency while providing a memorable user experience.

One of the major challenges faced by bots is that they cannot relate to humans. The ability to understand basic language and contexts is a significant issue for bots. For example, bots that quickly identify a customer service issue and resolve it are going to be far more useful than those repeatedly asking qualifying questions. But there are many bots that cannot understand the most basic commands or responses if they fall outside the planned conversational script. The result is an unfulfilling user experience. The use of Natural Language Processing(NLP) and Machine Learning is key to a positive conversation outcome. For bots to get better, Data Scientists need to program them to learn from the conversations they are having with users in real time. Initially, a bot may be limited in its ability to answer user queries, but it should gradually learn and improve. Using the customer data that has been gathered through bot-driven conversations will improve user experience exponentially.

Bots cannot solve everything. There is no dearth of unique requests, uncommon inquiries, and specific situations that chatbots may fumble. To remedy this issue as technology progresses, Data Scientists enlist the help of humans to work in unison with the bots as in the case of live chat sales. When a chatbot is presented with an inquiry that it cannot handle, it needs to be an expert at deciding when to engage a human operator. If this process is clumsy, the customer experience will be hampered. Additionally, Chatbots are sometimes used as a channel for spam. Many businesses use bots as another channel through which to send marketing content and deploy customer engagement campaigns. Bots comprise the conversational web, and the practice of using bots for spam goes against its main tenant: two-way interaction that presents a unique opportunity to develop connections with customers.

Data Scientists face several challenges in implementing chatbots, such as evaluating models, incorporating contexts, developing a coherent bot personality, and formulating suitable generic responses. The evaluation of a chatbot’s performance is based on their ability to perform tasks like solving a customer support problem or assisting a customer through a live chat sales transaction. Evaluating each bot model requires human judgment and analysis, an expensive resource. Furthermore, Data Scientists need to incorporate both the linguistic and physical contexts into bots to develop a sensible response system. When engaging in a long conversation, people not only remember what to say and how to say it, they also remember the content of the interaction. It is quite challenging to embed these multiple aspects of conversation into a vector, so Data Scientists incorporate context in order to help bots know how to interact and what to remember during a live chat sales conversation. Finally, in human conversation there are many phrases that use different words to mean the same thing. The chatbot’s response to semantically similar phrases must be consistent. Data Scientists call this fixed knowledge personality. Since generic responses are not trained on specific intention, they are used for different input cases. Although not a recommended practice in bot development today, the early versions of Google Smart Reply used the common phrase, “I Love You”, as a generic response to almost everything.

The advantages of chatbots will only continue to grow. But as new use cases increase, challenges will increase, too. As the synthesis of Data Science and Enterprises continues to enhance live chat sales – addressing topics such as user privacy, appropriate applications of bots, and optimizing human + bot implementation – bots and the conversational internet will undoubtedly make us more efficient at solving problems, implementing information, and navigating daily life.

About Dr. Michael Housman

Michael has spent his entire career applying state-of-the-art statistical methodologies and econometric techniques to large data-sets in order to drive organizational decision-making and helping companies operate more effectively.
Prior to founding RapportBoost.AI, he was the Chief Analytics Officer at Evolv (acquired by Cornerstone OnDemand for $42M in 2015) where he helped architect a machine learning platform capable of mining databases consisting of hundreds of millions of employee records. He was named a 2014 game changer by Workforce magazine for his work.
Michael is currently an equity advisor for a half-dozen technology companies based out of the San Francisco bay area: hiQ Labs, Bakround, Interviewed, Performiture, Tenacity, Homebase, and States Title. He was on Tony’s advisory board at Boopsie from 2012 onward.
Michael is a noted public speaker and has published his work in a variety of peer-reviewed journals and has had his research profiled by The New York Times, Wall Street Journal, The Economist, and The Atlantic.
Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.